import tensorflow as tf
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
#If you didn't set it, you will use all gpu automaticlly.
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#clear all
tf.reset_default_graph()
#define the variable
# a good habit is to define the name, and name scope
with tf.name_scope("algoritm1_variable"):
var1 = tf.Variable(np.zeros((2,3)),dtype=tf.float64,name="variable1")
var1 = tf.Variable(np.zeros((4,3)),dtype=tf.float64,name="variable1")
var2 = tf.Variable(np.zeros((4,3)),dtype=tf.float64,name="variable1")
print(var1)
print(var2)
#one graph
sess=tf.Session()
#initialization,very important
sess.run(tf.global_variables_initializer())
#put the number into it
var1_=sess.run(var1)
print(var1_)
tf.reset_default_graph()
var1 = tf.constant(np.zeros((2,3)),dtype=tf.float64)
print(var1)
#one graph
sess=tf.Session()
#initialization,very important
sess.run(tf.global_variables_initializer())
#put the number into it
var1_=sess.run(var1)
print(var1_)
tf.reset_default_graph()
#as input or output
features=84
#Why we set None? because sometimes we want to use different number of samples to train.
tf_x = tf.placeholder(tf.float32, [None,features])
print(tf_x)
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
tf.set_random_seed(1)
np.random.seed(1)
# fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise # shape (100, 1) + some noise
# plot data
plt.scatter(x, y)
plt.show()
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.float32, y.shape) # input y
# neural network layers
l1 = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
output = tf.layers.dense(l1, 1) # output layer
loss = tf.losses.mean_squared_error(tf_y, output) # compute cost
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)
train_op = optimizer.minimize(loss)
sess = tf.Session() # control training and others
sess.run(tf.global_variables_initializer()) # initialize var in graph
plt.ion() # something about plotting
for step in range(100):
# train and net output
_, l, pred = sess.run([train_op, loss, output], {tf_x: x, tf_y: y})
if step % 20 == 0:
print(l)
# plot and show learning process
plt.cla()
plt.scatter(x, y)
plt.plot(x, pred, 'r-', lw=5)
plt.text(0.5, 0, 'Loss=%.4f' % l, fontdict={'size': 20, 'color': 'red'})
plt.pause(0.1)
plt.ioff()
plt.show()
tf.set_random_seed(1)
np.random.seed(1)
# fake data
n_data = np.ones((100, 2))
x0 = np.random.normal(2*n_data, 1) # class0 x shape=(100, 2)
y0 = np.zeros(100) # class0 y shape=(100, )
x1 = np.random.normal(-2*n_data, 1) # class1 x shape=(100, 2)
y1 = np.ones(100) # class1 y shape=(100, )
x = np.vstack((x0, x1)) # shape (200, 2) + some noise
y = np.hstack((y0, y1)) # shape (200, )
# plot data
plt.scatter(x[:, 0], x[:, 1], c=y, s=100, lw=0, cmap='RdYlGn')
plt.show()
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.int32, y.shape) # input y
# neural network layers
l1 = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
output = tf.layers.dense(l1, 2) # output layer
#it is hard label here. but in the reality, reasonable soft label is always better, always!!
loss = tf.losses.sparse_softmax_cross_entropy(labels=tf_y, logits=output)
accuracy = tf.metrics.accuracy( # return (acc, update_op), and create 2 local variables
labels=tf.squeeze(tf_y), predictions=tf.argmax(output, axis=1),)[1]
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.05)
train_op = optimizer.minimize(loss)
sess = tf.Session()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op) # initialize var in graph
plt.ion() # something about plotting
for step in range(100):
# train and net output
_, acc, pred = sess.run([train_op, accuracy, output], {tf_x: x, tf_y: y})
if step % 20 == 0:
# plot and show learning process
plt.cla()
plt.scatter(x[:, 0], x[:, 1], c=pred.argmax(1), s=100, lw=0, cmap='RdYlGn')
plt.text(1.5, -4, 'Accuracy=%.2f' % acc, fontdict={'size': 20, 'color': 'red'})
plt.pause(0.1)
plt.ioff()
plt.show()